Special Issue "Advanced Communication and Networking Techniques for Remote Sensing"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (20 January 2020).

Special Issue Editors

Prof. Dr. Honghao Gao
E-Mail Website
Guest Editor
School of Computer Science and Engineering, Shanghai Unvisrity, Shanghai, China
Interests: edge computing; IoT ; swarm intelligence
Special Issues and Collections in MDPI journals
Dr. Xinheng Wang
E-Mail Website
Guest Editor
Unvisrity of West London, London W5 5RF, UK
Interests: service computing; cloud computing; business process management
Dr. Yuyu Yin
E-Mail Website
Guest Editor
Hangzhou Dianzi Unvisrity, Hangzhou, China
Interests: service computing; Artificial Intelligence
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing has become one of the most important methods used to quickly and directly acquire information on, above, or even below the Earth’s surface without direct contact with the objects examined. According to a new market intelligence report by BIS Research, the nano satellite market is expected to reach $356.1 million by 2023, growing at a CAGR of 13.43%. In recent years, remote sensing technologies have been applied widely in meteorology, climate change detection, environmental monitoring, flood prediction, agriculture, resource explorations, mapping, and so on. The requirements of different investigations have increased the heterogeneity and diversity of sensing devices. For example, a satellite can be classified as an optical satellite, microwave satellite, or multimode satellite. Meanwhile, the acquired sensing data are growing in volume, with a ground station’s daily data accumulation being able to reach the terabyte level. Hence, how to leverage such a huge number of devices to process/transmit large volumes of data to analyze and present them in the form of final knowledge is an important issue in the remote sensing field.

Communication and networking in remote sensing systems directly influence the acquired results and system overhead. Regardless of whether the transmission data captured are figures or device control commands, communication between different devices requires the support of efficient networks and protocols. Advanced communication and networking technologies should consider the geo-distribution of heterogeneous sensing devices, the mobility of potential smart sensors, the application of software-defined networking (SDN), the high bandwidth requirement, the overhead of massive data transmission, the tradeoff between communication and computation, the reliability/scalability of the network, and so on. The Special Issue aims to collect articles about recent research, experimental work, reviews, and/or case studies related to the field of communication and networking in remote sensing. Contributions may be focused on but not limited to the following topics:

  • Communication technologies in remote sensing;
  • Advanced networking technologies (e.g., SDN, specialized network) in remote sensing;
  • Network building in extreme environment;
  • Testbed and simulators for communication and networking in RS;
  • Performance evaluation and benchmarks for communication and networking in RS;
  • Security and privacy in communication and networking;
  • Communication model and protocols in RS;
  • Communication and networking resource management in RS;
  • Application of advanced technologies in RS;
  • Communication and networking service design and implementation in RS.
Dr. Honghao Gao
Dr. Xinheng Wang
Dr. Yuyu Yin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • Communication
  • Networking
  • Performance evaluation
  • Data processing
  • Data transmission
  • Sensors
  • Sustainable system
  • Security and privacy

Published Papers (1 paper)

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Open AccessArticle
Predicting Taxi Demand Based on 3D Convolutional Neural Network and Multi-task Learning
Remote Sens. 2019, 11(11), 1265; https://doi.org/10.3390/rs11111265 - 28 May 2019
Cited by 33 | Viewed by 1789
Taxi demand can be divided into pick-up demand and drop-off demand, which are firmly related to human’s travel habits. Accurately predicting taxi demand is of great significance to passengers, drivers, ride-hailing platforms and urban managers. Most of the existing studies only forecast the [...] Read more.
Taxi demand can be divided into pick-up demand and drop-off demand, which are firmly related to human’s travel habits. Accurately predicting taxi demand is of great significance to passengers, drivers, ride-hailing platforms and urban managers. Most of the existing studies only forecast the taxi demand for pick-up and separate the interaction between spatial correlation and temporal correlation. In this paper, we first analyze the historical data and select three highly relevant parts for each time interval, namely closeness, period and trend. We then construct a multi-task learning component and extract the common spatiotemporal feature by treating the taxi pick-up prediction task and drop-off prediction task as two related tasks. With the aim of fusing spatiotemporal features of historical data, we conduct feature embedding by attention-based long short-term memory (LSTM) and capture the correlation between taxi pick-up and drop-off with 3D ResNet. Finally, we combine external factors to simultaneously predict the taxi demand for pick-up and drop-off in the next time interval. Experiments conducted on real datasets in Chengdu present the effectiveness of the proposed method and show better performance in comparison with state-of-the-art models. Full article
(This article belongs to the Special Issue Advanced Communication and Networking Techniques for Remote Sensing)
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